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import requests, types, time |
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import json, uuid |
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import traceback |
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from typing import Optional |
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import litellm |
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import httpx, aiohttp, asyncio |
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from .prompt_templates.factory import prompt_factory, custom_prompt |
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class OllamaError(Exception): |
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def __init__(self, status_code, message): |
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self.status_code = status_code |
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self.message = message |
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self.request = httpx.Request(method="POST", url="http://localhost:11434") |
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self.response = httpx.Response(status_code=status_code, request=self.request) |
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super().__init__( |
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self.message |
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) |
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class OllamaConfig: |
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""" |
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Reference: https://github.com/jmorganca/ollama/blob/main/docs/api.md#parameters |
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The class `OllamaConfig` provides the configuration for the Ollama's API interface. Below are the parameters: |
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- `mirostat` (int): Enable Mirostat sampling for controlling perplexity. Default is 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0. Example usage: mirostat 0 |
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- `mirostat_eta` (float): Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. Default: 0.1. Example usage: mirostat_eta 0.1 |
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- `mirostat_tau` (float): Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. Default: 5.0. Example usage: mirostat_tau 5.0 |
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- `num_ctx` (int): Sets the size of the context window used to generate the next token. Default: 2048. Example usage: num_ctx 4096 |
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- `num_gqa` (int): The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b. Example usage: num_gqa 1 |
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- `num_gpu` (int): The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable. Example usage: num_gpu 0 |
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- `num_thread` (int): Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores). Example usage: num_thread 8 |
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- `repeat_last_n` (int): Sets how far back for the model to look back to prevent repetition. Default: 64, 0 = disabled, -1 = num_ctx. Example usage: repeat_last_n 64 |
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- `repeat_penalty` (float): Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. Default: 1.1. Example usage: repeat_penalty 1.1 |
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- `temperature` (float): The temperature of the model. Increasing the temperature will make the model answer more creatively. Default: 0.8. Example usage: temperature 0.7 |
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- `stop` (string[]): Sets the stop sequences to use. Example usage: stop "AI assistant:" |
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- `tfs_z` (float): Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. Default: 1. Example usage: tfs_z 1 |
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- `num_predict` (int): Maximum number of tokens to predict when generating text. Default: 128, -1 = infinite generation, -2 = fill context. Example usage: num_predict 42 |
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- `top_k` (int): Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. Default: 40. Example usage: top_k 40 |
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- `top_p` (float): Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. Default: 0.9. Example usage: top_p 0.9 |
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- `system` (string): system prompt for model (overrides what is defined in the Modelfile) |
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- `template` (string): the full prompt or prompt template (overrides what is defined in the Modelfile) |
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""" |
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mirostat: Optional[int] = None |
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mirostat_eta: Optional[float] = None |
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mirostat_tau: Optional[float] = None |
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num_ctx: Optional[int] = None |
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num_gqa: Optional[int] = None |
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num_thread: Optional[int] = None |
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repeat_last_n: Optional[int] = None |
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repeat_penalty: Optional[float] = None |
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temperature: Optional[float] = None |
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stop: Optional[ |
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list |
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] = None |
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tfs_z: Optional[float] = None |
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num_predict: Optional[int] = None |
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top_k: Optional[int] = None |
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top_p: Optional[float] = None |
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system: Optional[str] = None |
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template: Optional[str] = None |
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def __init__( |
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self, |
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mirostat: Optional[int] = None, |
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mirostat_eta: Optional[float] = None, |
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mirostat_tau: Optional[float] = None, |
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num_ctx: Optional[int] = None, |
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num_gqa: Optional[int] = None, |
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num_thread: Optional[int] = None, |
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repeat_last_n: Optional[int] = None, |
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repeat_penalty: Optional[float] = None, |
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temperature: Optional[float] = None, |
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stop: Optional[list] = None, |
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tfs_z: Optional[float] = None, |
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num_predict: Optional[int] = None, |
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top_k: Optional[int] = None, |
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top_p: Optional[float] = None, |
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system: Optional[str] = None, |
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template: Optional[str] = None, |
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) -> None: |
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locals_ = locals() |
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for key, value in locals_.items(): |
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if key != "self" and value is not None: |
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setattr(self.__class__, key, value) |
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@classmethod |
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def get_config(cls): |
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return { |
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k: v |
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for k, v in cls.__dict__.items() |
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if not k.startswith("__") |
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and not isinstance( |
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v, |
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( |
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types.FunctionType, |
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types.BuiltinFunctionType, |
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classmethod, |
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staticmethod, |
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), |
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) |
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and v is not None |
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} |
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def get_ollama_response( |
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api_base="http://localhost:11434", |
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model="llama2", |
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prompt="Why is the sky blue?", |
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optional_params=None, |
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logging_obj=None, |
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acompletion: bool = False, |
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model_response=None, |
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encoding=None, |
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): |
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if api_base.endswith("/api/generate"): |
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url = api_base |
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else: |
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url = f"{api_base}/api/generate" |
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config = litellm.OllamaConfig.get_config() |
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for k, v in config.items(): |
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if ( |
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k not in optional_params |
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): |
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optional_params[k] = v |
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optional_params["stream"] = optional_params.get("stream", False) |
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data = {"model": model, "prompt": prompt, **optional_params} |
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logging_obj.pre_call( |
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input=None, |
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api_key=None, |
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additional_args={ |
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"api_base": url, |
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"complete_input_dict": data, |
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"headers": {}, |
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"acompletion": acompletion, |
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}, |
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) |
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if acompletion is True: |
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if optional_params.get("stream", False) == True: |
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response = ollama_async_streaming( |
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url=url, |
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data=data, |
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model_response=model_response, |
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encoding=encoding, |
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logging_obj=logging_obj, |
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) |
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else: |
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response = ollama_acompletion( |
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url=url, |
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data=data, |
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model_response=model_response, |
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encoding=encoding, |
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logging_obj=logging_obj, |
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) |
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return response |
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elif optional_params.get("stream", False) == True: |
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return ollama_completion_stream(url=url, data=data, logging_obj=logging_obj) |
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response = requests.post(url=f"{url}", json=data, timeout=litellm.request_timeout) |
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if response.status_code != 200: |
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raise OllamaError(status_code=response.status_code, message=response.text) |
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logging_obj.post_call( |
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input=prompt, |
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api_key="", |
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original_response=response.text, |
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additional_args={ |
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"headers": None, |
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"api_base": api_base, |
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}, |
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) |
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response_json = response.json() |
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model_response["choices"][0]["finish_reason"] = "stop" |
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if optional_params.get("format", "") == "json": |
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message = litellm.Message( |
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content=None, |
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tool_calls=[ |
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{ |
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"id": f"call_{str(uuid.uuid4())}", |
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"function": {"arguments": response_json["response"], "name": ""}, |
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"type": "function", |
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} |
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], |
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) |
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model_response["choices"][0]["message"] = message |
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else: |
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model_response["choices"][0]["message"]["content"] = response_json["response"] |
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model_response["created"] = int(time.time()) |
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model_response["model"] = "ollama/" + model |
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prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(prompt))) |
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completion_tokens = response_json["eval_count"] |
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model_response["usage"] = litellm.Usage( |
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prompt_tokens=prompt_tokens, |
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completion_tokens=completion_tokens, |
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total_tokens=prompt_tokens + completion_tokens, |
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) |
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return model_response |
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def ollama_completion_stream(url, data, logging_obj): |
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with httpx.stream( |
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url=url, json=data, method="POST", timeout=litellm.request_timeout |
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) as response: |
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try: |
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if response.status_code != 200: |
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raise OllamaError( |
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status_code=response.status_code, message=response.text |
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) |
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streamwrapper = litellm.CustomStreamWrapper( |
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completion_stream=response.iter_lines(), |
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model=data["model"], |
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custom_llm_provider="ollama", |
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logging_obj=logging_obj, |
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) |
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for transformed_chunk in streamwrapper: |
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yield transformed_chunk |
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except Exception as e: |
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raise e |
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async def ollama_async_streaming(url, data, model_response, encoding, logging_obj): |
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try: |
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client = httpx.AsyncClient() |
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async with client.stream( |
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url=f"{url}", json=data, method="POST", timeout=litellm.request_timeout |
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) as response: |
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if response.status_code != 200: |
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raise OllamaError( |
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status_code=response.status_code, message=response.text |
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) |
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streamwrapper = litellm.CustomStreamWrapper( |
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completion_stream=response.aiter_lines(), |
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model=data["model"], |
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custom_llm_provider="ollama", |
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logging_obj=logging_obj, |
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) |
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async for transformed_chunk in streamwrapper: |
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yield transformed_chunk |
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except Exception as e: |
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traceback.print_exc() |
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async def ollama_acompletion(url, data, model_response, encoding, logging_obj): |
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data["stream"] = False |
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try: |
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timeout = aiohttp.ClientTimeout(total=litellm.request_timeout) |
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async with aiohttp.ClientSession(timeout=timeout) as session: |
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resp = await session.post(url, json=data) |
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if resp.status != 200: |
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text = await resp.text() |
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raise OllamaError(status_code=resp.status, message=text) |
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logging_obj.post_call( |
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input=data["prompt"], |
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api_key="", |
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original_response=resp.text, |
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additional_args={ |
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"headers": None, |
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"api_base": url, |
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}, |
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) |
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response_json = await resp.json() |
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model_response["choices"][0]["finish_reason"] = "stop" |
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if data.get("format", "") == "json": |
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message = litellm.Message( |
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content=None, |
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tool_calls=[ |
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{ |
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"id": f"call_{str(uuid.uuid4())}", |
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"function": { |
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"arguments": response_json["response"], |
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"name": "", |
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}, |
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"type": "function", |
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} |
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], |
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) |
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model_response["choices"][0]["message"] = message |
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else: |
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model_response["choices"][0]["message"]["content"] = response_json[ |
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"response" |
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] |
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model_response["created"] = int(time.time()) |
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model_response["model"] = "ollama/" + data["model"] |
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prompt_tokens = response_json.get("prompt_eval_count", len(encoding.encode(data["prompt"]))) |
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completion_tokens = response_json["eval_count"] |
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model_response["usage"] = litellm.Usage( |
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prompt_tokens=prompt_tokens, |
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completion_tokens=completion_tokens, |
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total_tokens=prompt_tokens + completion_tokens, |
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) |
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return model_response |
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except Exception as e: |
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traceback.print_exc() |
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raise e |
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async def ollama_aembeddings( |
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api_base="http://localhost:11434", |
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model="llama2", |
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prompt="Why is the sky blue?", |
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optional_params=None, |
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logging_obj=None, |
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model_response=None, |
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encoding=None, |
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): |
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if api_base.endswith("/api/embeddings"): |
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url = api_base |
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else: |
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url = f"{api_base}/api/embeddings" |
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config = litellm.OllamaConfig.get_config() |
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for k, v in config.items(): |
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if ( |
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k not in optional_params |
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): |
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optional_params[k] = v |
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data = { |
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"model": model, |
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"prompt": prompt, |
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} |
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logging_obj.pre_call( |
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input=None, |
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api_key=None, |
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additional_args={"api_base": url, "complete_input_dict": data, "headers": {}}, |
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) |
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timeout = aiohttp.ClientTimeout(total=litellm.request_timeout) |
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async with aiohttp.ClientSession(timeout=timeout) as session: |
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response = await session.post(url, json=data) |
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if response.status != 200: |
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text = await response.text() |
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raise OllamaError(status_code=response.status, message=text) |
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logging_obj.post_call( |
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input=prompt, |
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api_key="", |
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original_response=response.text, |
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additional_args={ |
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"headers": None, |
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"api_base": api_base, |
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}, |
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) |
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response_json = await response.json() |
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embeddings = response_json["embedding"] |
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output_data = [] |
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for idx, embedding in enumerate(embeddings): |
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output_data.append( |
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{"object": "embedding", "index": idx, "embedding": embedding} |
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) |
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model_response["object"] = "list" |
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model_response["data"] = output_data |
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model_response["model"] = model |
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input_tokens = len(encoding.encode(prompt)) |
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model_response["usage"] = { |
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"prompt_tokens": input_tokens, |
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"total_tokens": input_tokens, |
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} |
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return model_response |
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